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Answers to the questions buyers ask before spending on AI hardware. Each guide opens with the short answer, shows the reasoning, and is dated and sourced.

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BuyingAI workstation vs AI server: how to choose in MalaysiaWho uses it, where it lives, and how it is powered and managed decide between a workstation, a departmental tower and a rack server. A plain comparison with the questions to answer first.Updated 27 Oct 2026

SizingHow much VRAM do you need for a local LLM?Work out GPU memory for a local language model from its weights, the KV cache for your context and users, and runtime overhead, with worked examples and the formula behind them.Updated 27 Oct 2026

Cooling & powerPassive vs active GPU cooling: what server buyers must knowWhy a passive server GPU overheats in a desktop case, what a server chassis provides instead, and how to match workstation and server editions to the machine and the room.Updated 27 Oct 2026

CostLocal AI vs cloud GPU: practical cost and control tradeoffsHow to compare owning AI hardware with renting cloud GPUs or paying per token: every cost to count, a break-even method you can fill with your own figures, and the control questions that decide it.Updated 27 Oct 2026

AgentsChoosing hardware for AI coding agents and 24/7 automationWhether an agent machine needs a GPU depends on where the model runs. How to size a local model for parallel agents with long context, and what makes a machine reliable enough to run unattended.Updated 27 Oct 2026

BuyingQuestions to ask before buying an enterprise GPU serverA checklist for IT and procurement: workloads and users, facility power and cooling, GPU and topology, software compatibility, management and security, and delivery and support.Updated 27 Oct 2026

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